Modeling user concerns in Sharing Economy: the case of food delivery apps

Abstract

Sharing Economy apps, such as Uber, Airbnb, and TaskRabbit, have generated a substantial consumer interest over the past decade. The unique form of peer-to-peer business exchange these apps have enabled has been linked to significant levels of economic growth, helping people in resource-constrained communities to build social capital and move up the economic ladder. However, due to the multidimensional nature of their operational environments, and the lack of effective methods for capturing and describing their end-users' concerns, Sharing Economy apps often struggle to survive. To address these challenges, in this paper, we examine crowd feedback in ecosystems of Sharing Economy apps. Specifically, we present a case study targeting the ecosystem of food delivery apps. Using qualitative analysis methods, we synthesize important user concerns present in the Twitter feeds and app store reviews of these apps. We further propose and intrinsically evaluate an automated procedure for generating a succinct model of these concerns. Our work provides a first step toward building a full understanding of user needs in ecosystems of Sharing Economy apps. Our objective is to provide Sharing Economy app developers with systematic guidelines to help them maximize their market fitness and mitigate their end-users' concerns and optimize their experience.

Use Python Data Analysis to Gain Insights from Airbnb Hosts

“…However, sentiment analysis can be hard to finish and can be easily compromised. To get accurate results, the author must eliminate all the nonvariables and dummy variables to make the dataset clean [17].…”

Analysing app reviews for software engineering: a systematic literature review

“…Relevant information can be found at any location in the reviews. For instance, a problematic feature can be discussed in a middle of a sentence (Guzman and Maalej 2014;Williams et al 2020), or a requested improvement can be expressed anywhere in a review Guo and Singh 2020).…”

“…User intentions include reporting an issue or requesting a new feature (Panichella et al 2015;Panichella et al 2016;Srisopha et al 2020b). Discussion topics include a variety of concerns such as installation problems, user interface, or price (Mujahid et al 2017;Ciurumelea et al 2018;Williams et al 2020); topics concerning user perception e.g., rating, user experience or praise (Pagano and Maalej 2013; (Khalid 2013;McIlroy et al 2016;Tao et al 2020). For instance, review classification has been proposed to detect different types of usability and user experience issues (Bakiu and Guzman 2017;Alqahtani and Orji 2019), quality concerns (Mercado et al 2016;Wen and Chen 2020) or different types of security and privacy issues (Cen et al 2014;Tao et al 2020).…”

“…Clustering is thus widely used as an exploratory analysis technique to infer topics commonly discussed by users (Pagano and Maalej 2013;Guzman and Maalej 2014;Liu et al 2018) and aggregate reviews containing semantically related information (Chen et al 2014;Palomba et al 2017;Zhou et al 2020). Clustering can be used for grouping reviews that request the same feature (Peng et al 2016;Di Sorbo et al 2016), report similar problems (Martin et al 2015;Villarroel et al 2016;Gao et al 2018b;Williams et al 2020), or discuss a similar characteristic of the app (Vu et al 2016;Chen et al 2019;Xiao et al 2020). The generated clusters might help software engineers synthesize information from a group of reviews referring to the same topics rather than examining each review individually (Fu et al 2013;Hadi and Fard 2020).…”

“…For example, studies have analysed the relation between user ratings and the vocabulary and length of their reviews (Hoon et al 2012;Vasa et al 2012). Studies have shown that users discuss diverse topics in reviews (Pagano and Maalej 2013;Shams et al 2020), such as app features, qualities (Williams and Mahmoud 2018;Franzmann et al 2020), requirements or issues (Khalid 2013;Alqahtani and Orji 2019;Kalaichelavan et al 2020;Williams et al 2020). For example, using content analysis, researchers analysed recurring types of issues reported by users (McIlroy et al 2016;Wang et al 2020a;Shams et al 2020), their distribution in reviews as well as as relations between app issue type and other information such as price and rating (Iacob et al 2013b; or between issue type and code quality indicators (Di Sorbo et al 2020).…”